A Simulation Model for Potato Growth and Development ...deficit are simulated in SUBSTOR-Potato and...

A Simulation Model for Potato Growth and Development SUBSTOR-Potato Version 20

Timothy SGriffin Bradley SJohnson and Joe T Ritchie

9

IBSNAT Research Report Series 02 Repo rtSeri e 02-am 4-MIs

Published for distribution by the IBSNAT Project

Michigan State University Department of Crop and Soil Sciences East Lansinamp Michigan 48824-1325

and

University of Hawaii College of Tropical Agriculture ampHuman Resources Department of Agronomy and Soil Science Honolulu Hawaii 96822

Support for the IBSNAT Project isprovided through a cooperative agreement (DAN-4054-A-o0-7081-00) between the USAgency for International Development and the University of Hawaii All reported opinions conclusions and recommenshydations are those of the authors and not those of the funding agency or the United States government

Corect citation IBSNAT 1993 Research Report Series 02 A Simulation Model for Potato Growth and DevelopmentSUBSTOR - Potato Version 20 Dept of Agronomy and Soil Science College of TropicalAgriculture and Human Resources Univ of Hawaii Honolulu HI

IBSNAT Research Report Series 02--593 (500)


Timothy SGriffin Cooperative Extension and Department of Plant

Soil and Environmental Sciences University of Maine Orono ME 04469

Bradley SJohnson Department of Crop and Soil Science

Michigan State University East Lansing MI 48824

Joe T Ritchie Department of Crop and Soil Science


IBSNAT Research Report Series 02

SUBSTOR-Potato Version 20

Introduction

The accumulation and partitioning of biomass and the phenological development of a potato (Solanumtuberosum L) crop are influenced by many factors Individual or in combination the most important are the environmental variables temperature (Snyder and Ewing 1989 Prange et al 1990) photoperiod or daylength (Ewing 1981 Wheeler and Tibbetts 1986) and intercepted radiation (MacKerron and Waister 1985) The simulation of potato growth across diverse environments and different cultivars must take each of these variables into account Numerous efforts have been made to simulate potato growthranging from simple regression or correlative models based on temperature (eg Iritani 1963 Manrique ind Bartholomew 1991) to the mechanistic organ-level model of Ng and Loonis (1984) A common feature of these models is that they are location- (and usuallycultivar-) specific 3y limiting the application of the model to a specific geographical region the number L f environmental inputs required to run the model is effectively reshyduced Temperature -esponsefunctions (ie thermal time or growing degree days (GDD)) are most commonly used to simulate both growth and development Manrique and Bartholomew (1991) demonstrated that changes in biomass partitioning of cvs Kennebec and Desiree in Hawaii were strongly related to a single environmental factor minimum daily temperature The model of Ingram and McCloud (1984) for cv Sebago in Floridadepends almost entirely on temperature response to determine potential growth rate but is rather unique in using different functions for the growth of roots tops and tubers Hartz and Moore (1978) and Sands et al (1979) use temperature as the primary factor in estimatshying potential biomass accumulation and adjust these estimates based on intercepted radiashytion MacKerron and Waister (1985) were able to accurately predict the growth of cv Maris Piper in Scotland using total intercepted radiation not temperature as the primary factor presumably because daily temperatures in this region are near the )ptimum for potato growth (ie 17 to 20C) during tuber initiation and bulking

The effect of photoperiod is ignored in most potato models although tuber initiation is sensitive to photoperiod and wide differences in photoperiod response between cultivars have been demonstrated (eg Ben Khedher and Ewing 1985 Snyder and Ewing 1989)There are two potential reasons for this omission First the aforementioned geographic and cultivar specificity makes it unnecessary to quantify the effects of different photoperiods (vs predictable seasonal shifts in photoperiod) on each cultivar And second unlike temperature response where cardinal values are reasonably well documented the quantifishycation of photoperiod effects has been less rigorous For most cultivars a threshold photoshyperiod beyond which tuber initiation is adversely affected has not been firmly established


This paper presents the development and performance of a new potato model SUBSTOR-Potato Version 20 which is intended to be used over a wide geographical range and for different cultivars SUBSTOR-Potato was developed as a CERES-type crop model and thus uses capacity type models of soil water and soil N dynamics that are used in other CERES-type models (eg Jones and Kiniry 1986) The effects of soil water and plant N deficit are simulated in SUBSTOR-Potato and are used to modify rates of growth and phenoshylogical development but the details of these calculations are not presented in this paper Ins-ead this paper focuses on the effects of temperature photoperiod and light intercepshytion on development and biomass accumulation and partitioning by potato The input files for soil climatic and cultural data required as input to run the model are similar to those for other CERES-type models and are described in IBSNAT Technical Report 5 (IBSNAT 1986) Thornton et al (1991) and Ritchie et al (1992) The following discussion includes I prediction of phenolshyogy UIprediction of biomass accumulation and partitioning and III model performance

Model Development

Relative Temperature Functions (subroutine THTIME)

SUBSTOR-Potato uses zero-to-one relative temperature functions based on mean daily air temperature (XTEMP variable descriptions in Table 1) or soil temperature (ST(LO)) to simulate the response of different plant organs and processes over a wide temperature range The relative temperature factors (RTF) increase from zero at some base temperature (TB from 2 to 5degC) to a plateau value of one then decrease to zero at temperatures of 33 to 35degC depending on the function Manrique and Hodges (1989) demonstrated that this type of function was preferable to a linear temperature function because it accounted for the obvious detrimental effect of high temperatures on potato growth and development

The relative temperature factors for vine growth (RTFVTNE) and tuber and root growth (RTFSOIL) are illustrated in Figure 1 The RTFSOIL function (Equation 1)is adapted from the seed piece substrate availability function of Ingram and McCloud (1984) and is used in the model for substrate mobilization from the seed piece root growth and tuber growth The RTFVINE function (Equation 2) is generalized using cardinal values from numerous literature sources (eg Yamaguchi et al 1964 Epstein 1966 Marinus and Bodleander 1975 Moorby and Milthorpe 1975 Prange et al 1990) This function is used to calculate daily leaf expansion and vegetative biomass accumulation We agree with the Jngran and

2


Table 1 Summary of simulatedand state variablesIn SUBSTOR-Potato

Variable name Units

Subroutine PHENOL (Phenological development)

CTH none GRORT g plant 1 d1

PHPER hr RDLFMI none

RTFTI none

SEEDAV g plant 1

ST(LO) degree C TEMPM degree C

TEMPMN degree C TEMPMX degree CTII none

Subroutine THTIME (Calculation of relative thermal time)

RTFSOIL none

RTFVINE none

XTEMP degree C

Subroutine GROSUB (Carbon assimilation and partitioning)

CARBO g plant- d- 1

DDEADLF g plant-1

DEVEFF none

Description

Cumulative tuber induction index (TII) Daily root growth Length of photoperiod Relative daylength factor for tuber

initiation Relative temperature factor for tuber

initiation Available seed reserve 08seed weight

at planting Mean daily soil temperature (surface) Weighted mean daily air temperature

075TEMPMN+025TEMPMX Minimum daily air temperature (input) Maximum daily ain temperature (input)Tuber induction index strength of

induction to tuberize

Relative temperature factor for tuber amp root growth

Relative temperature factor for vine growth

Mean daily air temperature

Actual daily carbon assimilation Weight of daily leaf loss from senescence Developmental effect for partitioning

during transition from ISTAGE1 to ISTAGE 2


GROLF g plant- I d1 Daily leaf growth GROPLNT g plant- d- Daily total plant growth

d-lGRORT g plant-1 Daily root growth GROSTM g plant-1 d-1 Daily stem growth

d-1GROTUB g plant-1 Daily tuber growth 2 2LAI m m Leaf area index

2 1LALWR 270 cm g- Leaf area to leaf weight PAR MJ M-2 Photosynthetically active adiation

d-1PCARB g plant-1 Potential carbon assimilation from photosynthesis

PLA cm 2 plant-1 Plant leaf area PLAG cm 2 plant1 Daily plant leaf area growth PLAS cm 2 plant- I Daily plant leaf area senesced due to

stress PRFT none Photosynthetic reduction factor for

temperaturePTUBGR g plant-1 d-l Maximum potential daily tuber growth RLGR none Relative leaf growth rate

RVCHO g plant- I Reserve soluble carbohydrate pool TIND none Proportion of PTUBGR receiving first

priority

Subrowine NFACTO (Nitrogen deficit factors)

NDEF1 none Relative N deficit effect on

photosynthesis NDEF2 none Relative N deficit effect on growth NFAC none Leaf N concentration relative to TCNP

and TMNC TANC Actual N concentration in vines TCNP Critical vine N concentration TMNC Minimum vine N concentration below

which growth ceases


100

o0O Figure1

Relative thermal time

040 functions for vine growth (RTFVINE)

000 and for tuber0 5 10 15 20 25 30 35 40 and root Mean Daily Air Temperature (XTM) growth

(RTFSOIL)

100

OJO

= 060

040

020

000 A I i I I I

0 5 10 15 20 25 30 35 40

Mean Daily Soil Temperature (ST(LO))

McCloud (1984) conclusions that potato leaves tubers and roots have different temperature response functions However there are insufficient data to distinguish separate temperashyture response functions for growth rate and development rate (Sands et al 1979) Thus RTFVINE is also used to calculate the rate of phenological development

RTFSOIL = 0 if STlt=2 or STgt33 [1] = 0079 (ST-2) if 2ltSTlt=15 = 1 if 15ltSTlt=23 = 1 - 01 (ST-23) if 23ltSTlt=33

RTFVINE =0 if XTEMPlt=2 or XTEMPgt35 [2] = 00667 (XTEMP-2) if 2ltXTEMPlt=17 = 1 IF 17ltXTEMPlt=24 = 1 -00909 (XTEMP-24) if 24ltXTEMPlt=35


Phenological Development (subroutine PHENOL)

Growth Stages Potato growth is divided into five phenological stages (variable ISTAGE) within SUBSTOR-Potato as follows

ISTAGE 5 pre-planting ISTAGE 6 planting to sprout germination ISTAGE 7 sprout germination to emergence ISTAGE 1 emergence to tuber initiation ISTAGE 2 tuber initiation to maturity

The variable XSTAGE is used to mark progression through each ISTAGE It is calculated as a function of accumulated RTFVINE during ISTAGES 567 and 2 and as a function of RTFVINE and photoperiod during ISTAGE 1

Sprout Germination and Emergence In SUBSTOR-Potato preference is given to observed emergence dates (IEMERG) to be included in the appropriate input file rather than simulation of sprout germination and emergence We based this decision on the paucity of reliable calibration data for preshyemergent development and more importantly the inherent difficulty in obtaining accurate assessments of seed piece physiological age that affects the rate at which potato reaches both of these phenological events (Greenwood et al 1985a Van der Zaag and Van Loon 1987) If IEMERG is not input germination of unsprouted seed and sprout elongation of both sprouted and unsprouted seed are simulated using RTFSOIL For both unsprouted and sprouted seed emergence occurs when cumulative sprout length (SPRLEN) gt depth of planting (SDEPTH)

Tuber Initiation Mechanistic simulation models for potato (eg Ng and Loomis 1984) have attempted to model the timing rate and duration of tuber initiation Because of the multitude of factors affecting tuber initiation and the lack of understanding of how these factors affect initiation at the physiological level we have taken an approach suggested by Sands et al (1979) to estimate the timing of tuber initiation that is extrapolation of linear tuber bulking rate back to the time axis (zero tuber weight) This appruach makes tuber initiation an instantaneous event like emergence rather than a distinct growth stage Because the initial lag phase of tuber growth is not directly simulated in SUBSTOR-Potato the estimated date of tuber initiation will by necessity be later than the observed date

Tuber initiation effectively divides the post-emergence growing season into vegetative and tuber-bearing stages and an accurate estimation of when initiation occurs is critical Plant


leaf area at initiation and thus the plants ability to intercept radiation during tuber bulkshying strongly influences end-of-season tuber yield when nutrients and water are not limitshying during bulking (Moorby and Milthorpe 1975 MacKerron and Waister 1985) In SUBSTOR-Potato the timing of tuber initiation is a function of cultivar response to both temperature and photoperiod with these responses modified by plant N status and soil water status In developing the theoretical framework for predicting tuber initiation we have relied heavily on the theory put forth by Ewing (1981) Wheeler and Tibbetts (1986) and others that tuber initiation by early cultivars is less sensitive to non-optimal condishytions (ie high temperatures andor long photoperiods) than initiation by late cultivars

Researchers have established that (i)cultivars differ in the threshold temperature above which tuber initiation is inhibited (Ingram and McCloud 1984 Ben Khedher and Ewing 1985 Snyder and Ewing 1989) and (it) tuber initiation is influenced more strongly by daily minimum temperature than by daily mean or maximum temperature (Slater 1968 Manrique and Bartholomew 1991) We developed a dimensioinless cultivar-spccLT relative temperature factor (RTFTI range of 0 to 1)to simulate the effect of high temperashytures on tuber initiation This function is similar in shape to the RTFVINE function Cultishyvars are assigned a coefficient for critical temperature (1C Table 2) above which tuber initiation is inhibited to some degree Cultivar TC corresponds roughly to early versus late with early cultivars having a higher value for TC The RTFTI value above TC is

Table 2 Genetic coefficientsin SUBSTOR-Potato forleaf expanslonrate (G2) tuber growth rate (G3) determinacy (PD) and sensitivityoftuber initiation to photoperiod (P2) and temperature

Cultivar G2 G3 PD P2 TC

2 2 d-1 2 d-1Units cm m- gm C

Segago 20000 225 07 08 150 Russet Burbank 20000 225 06 06 170 Katahdin 20000 250 07 06 190 Mars Piper 20000 250 08 04 170 Desiree 20000 250 09 06 170 Norchip 20000 250 10 04 170


Figure 2

100 -Relative thermal time function for

080 effect of temperashyture on tuber Initiation (RTF71

060- is the relative temperaturefactor fortuberinitiation

o4o TC is thecuitivarshyspecific critical

020 0- TC =17 temperatureabove which tuber

a--amp TC =19 0-O TC = 21 initiationis inhibshy

000 I0-0 TC = 23 ited TEMPMIN 0 5 10 15 20 25 30 35 and TEMPMAXare

minimum and TempMin075 + iempMax025 maximum dailyair

temperatures)

calculated as

RTFTI = 10-00156(TEMPM-TC) 2 TCltTEMPMltTC+8 [31 RTFTI = 0 TEMPMgtTC+8 where TEMPM = 075TEMPMN+025TEMPMX [4]

where TEMPMN and TEMPMX are daily minimum and maximum temperatures respecshytively The result (Figure 2) is a family of identical decreasing curves dependent on a single coefficient TC

The calculation of a relative daylength factor (RDLFTI) is similar to that for RTFTI When the photoperiod is less than 12 hr RDLFTI equals 10 for all cultivars This common photoshyperiod insensitive phase was demonstrated by Rasco et a] (1980) who showed that differshyent cultivars initiate tubers at about the same time under favorable (ie short) photoperiod Differences in time to tuber initiation become apparent under long photoperiods or as discussed above under high temperatures

For photoperiods greater than 12 hr early and late cultivars are differentially sensitive to increasing photoperiod with early cultivars being less sensitive than late cultivars (Ewing


1981 Wheeler and Tibbetts 1986 Snyder and Ewing 1989) Thus early cultivars should have a higher RDLFTI under long (16 to 20 hr) photoperiods To accommodate these differences each cultivar is assigned a dimensionless genetic coefficient (P2) indicating sensitivity of tuber initiation to photoperiod This coefficient effectively ranging from 02 to 08 defines the shape of the RDLFTI curve by

RDLFTI = (10-P2)+000694P2(240-PHPER) 2 [51

where PHPER is photoperiod (in hrs) The resultant family of RDLFTI curves is shown in Figure 3 Examples of the upper- and lowermost RDLFTI curves are Norland and Andigena respectively (Wheeler and Tibbetts 1986 Rasco et al 1980)

A tuber induction index (TII) is calculated daily as

TII = (RTFTIRDLFTI)+05(10-AMINI (SWDF2NDEF2)) [6]

and is used as a measure of the relative strength of the induction to tuberize AMIN1 is the FORTRAN command for selecting the minimum value from a list of numerical values The modifiers SWDF2 and NDEF2 are factors for soil water and N stress on expansion growth These factors through their effect on TiI hasten tuber initiation under stress conditions

100 Figure 3 Functionfor relative effect

080 - ofphotoperiod on tuber

060 Initiation (RDLFTI is the

0daylength relative

factor fortuber 02 - =04 amp- 12 =06

Initiation P2 is the cultivarshy

6 9 12 is 16 21 24

D-0--ao-

P2 = 08 =10 specificgenetic

coefficient)

Photoperiod (hr)

9


The multiplicative function of RTFTI and RDLFII is necessary because long photoperiods and high temperature act synergistically in inhibiting tuber initiation (Snyder and Ewing 1989 Ewing et al 1990)

From the daily calculation of TII (beginning at emergence) a cumulative tuber induction index (CTII) is calculated as

C171 = ITII [7]

Available calibration data sets indicate that zero tuber weight obtained by extrapolation of linear bulking back to the time axis corresponds to a CTII value of approximately 20 Thus this value is used to flag tuber initiation in SUBSTOR-Potato

Biorrass Accumulation and Partitioning (subroutine GROSUB)

Pre-emergent Growth (ISTAGE 7) The seed piece represents the only carbon (C)source to support growth from sprout germishynation to emergence Growth during this stage is simulated only if IEMERG is not supshyplied by the user Maximum availability of seed piece C (SEEDAV) for growth is adapted from Ingram and McCloud (1984) and pre-emergent sprout growth (SPRWT) is a linear function of RTFSOIL Daily root growth (GRORT) is assumed to equal daily SPRWT If the sum of sprout and root growth exceeds SEEDAV the growth of both components is reshyduced by the fraction SEEDAV(SPRWT + GRORT)

Vegetative Growth (ISTAGE 1) Post-emergent growth is supported by three C sources seed reserves current photosynshythetic assimilate and reserve carbohydrate Immediately following emergence growth is supported primarily by seed reserves As in Ng and Loomis (1984) the availability of seed reserve decreases as plant leaf area (PLA) increases This is accounted for in the model by calculating SEEDAV as a function of PLA up to 400 cm 2plant -1 after which SEEDAV is zero

Potential photosynthetic C assimilation (PCARB) is calculated as

PCARB = 35PARPLANTS(10EXP(-055LAI)) [81

where PAR is photosynthetically active radiation (MJ m 2)EXP is the FORTRAN comshymand for an exponential function and LAI is leaf area index (m2m-2) The radiation use efficiency (RUE) of 35 g MJ1 PAR is adapted from literature values (Sale 1973 Allen and

10

SUBS TOR-Potato Version 20

Scott 1980 Jeffries and MacKerron 1989 Manrique et al 1991) as is the extinction coeffishydent k of -055 (Allen and Scott 1980) The above calculation estimates net C assimilation assuming that nutrients and soil water are non-limiting Actual C assimilation (CARBO) is calculated by

CARBO =PCARBAMINI(PRFTSWDF1NDEF1))+05DDEADLF [9]

The unitless modifiers are for temperature (PRFT) soil water (SWDF1) and N stress (NDEF1) effects on photosynthetic efficiency One-half of the C in senesced leaves (DDEADLF) is translocated prior to abscission (Johnson et al 1986) If CARBO is greater than daily growth demand excess C enters a soluble carbohydrate pool (RVCHO) limited to 10 percent of haulm dry weight (Ng and Loomis 1984)

The following priorities for C use are used in SUBSTOR-Potato Photosynthetic assimilate is always used first If additional C is needed to meet growth demand either seed reserves or carbohydrate reserves may be used according to the criteria described above The use of see reserves and carbohydrate reserves are mutually exclusive in the model because the carbohydrate reserve is allowed to accumulate when PLA gt 400 cm 2 plant -1 when seed reserve is no longer available

Diring vegetative growth potential leaf expansion (PLAG) is calculated first as

PLAG = EXP(RLGR)PLA-PLA [101 RLGR = 050RTFVINE [111 GROLF = PLAG (1LALWR) [121

where RLGR is the relative leaf growth rate (Ingram and McCloud 1984) This estimate of leaf expansion may be modified for N stress and soil water stress effects on expansion growth (NDEF2 and SWDF2) and is then used to calculate daily biomass addition to leaves (GROLF) based on a leaf area to leaf weight ratio (LAWLR) of 270 cm 2 g-1 Daily stem growth (GROSTM) is assumed to equal GROLF while partitioning of biomass for root growth (GRORT) changes with phenological stage Growth of all organs are given equal priority during ISTAGE 1 (eg Munns and Pearson 1974) Thus a shortage of C for growth results in all organ growth potentials being reduced by the fraction CARBO GROPLNT where GROPLNT is the summation of GROLF GROSTM and GRORT

Tuber-bulking (ISTAGE 2) The initiation of tubers elicits several fundamental changes in the growth of the potato plant First as shown by Sale (1973) radiation use efficiency (RUE) may increase by 50

1


percent or more Sale attributed this increase in RUE to the presence of the tubers which represented a large rapidly growing sink for photosynthetic assinilate Rather than attempting to dynamically simulate the effect of sink size on RUE SUBSTOR-Potato

1calculates PCARB during tuber bulking using [81 with the RUE increased to 40 g MJ

PAR Actual carbon assimilation (CARBO) is calculated as in [9]

The second change in growth after tuber initiation involves bi amass partitioning to comshypeting organs or sinks Unlike growth during ISTAGE 1when the proportion of total assimilate partitioned to each organ remains relatively constant under stressed vs nonshylimiting conditions partitioning during ISTAGE 2 is a dynamic process potentially influshyenced by many factors For example partitioning to tubers is favored (ie a greater proporshytion of the total is allocated to tubers) by low temperature short photoperiod and low to modei ate soil water or N level Because these same factors hasten tuber initiation Ewing (1981) suggested that the tuberization stimulus influences both initiation and partitioning of biomass after initiation To make this partitioning response operational in SUBSTOR-Potato we assume that tubers are given first priority on available assimilate (from both current photosynthesis and the soluble reserve carbohydrate pool) thereby eliminating the need to directly estimate partitioning coefficients to allocate C When the tuber sink is small or conditions are non-limiting this assumption is not critical because nearly all daily growth demands can be met However when stress reduces the amount of assimilate available or the tuber sink is very large growth of vines and roots can be greatly reduced by imposing this priority This was demonstrated experimentally by Munris and Pearson (1974) who found that drought during tuber bulking could cause a very rapid cessation of vine growth while tubers continued to grow

Environmental and soil factors are used in SUBSTOR-Potato to modify potential tuber growth demand The estimation of tuber growth demand is a two-step process The first step is to estimate the proportion of maximum potential tuber growth that will receive first priority on assimilate (variable TIND) TIND is independent of the size of the tuber sink and is based on the concept that tuber sink strength is analogous to tuber induction strength TIND is calculated as

TIND = (YDTII3)(l NFAC)DEVEFF when NFACgt1 [13] TIND = (YDTII3)DEVEFF when NFAClt1

where DTII = RTFTI+05((10-AMINI(SWDF2NDEF210)) [141 DEVEFF = AMINI ((XSTAGE-Z0)1 0PDi 0)

12


DTII estimates the daily tuber sink strength as a function of temperature and nutrient and soil water status A t~iree-day moving average of DTH is used because the partitioning response to changing conditions is not instantaneous (Ewing 1981) and serves to buffer against excessive fluctuations in partitioning NFAC which is used to calculate N deficit factors is included only when greater than one indicating the promotion of vegetative growth by excessive N DEVEF is an artificial variable that alters partitioning during the transition from vegelative to tuber-beaing stages For example for determinate cultivars (PD = 1) DEVEFF equals 10 when XSTAGE equals 21 (ie approximately one week after initiation)

Estimation of potential and actual tuber growth rat2 (PTUBGR and GROTUB) is the second step PTUBGR is a function of maximum tuber growth rate (genetic coefficient G3) and temperature in the forei

PTUBGR = G3RFFSOILPLANTS [15]

Tuber growth potential leaf expansion (PLAG) and iLZ~f stem and root growth (GROLF GROSTM and GRORT) are then calculated as

GROTUB = PTUBGRAMIN1(SWDF2NDEF210)TIND [16] PLAG = (G2RFTVINEPLANTS)AMIN1(SWDF2NDEF210)

GROLF = PLAGLALWR GROSTM = GROLF075 GRORT = (GROLF+GROSTM)02

G2 is a genetic coefficient for maximum leaf expansion rate currently equal to 2 shy2000 crn m 2 d-1 for all cultivars because of the lack of evidence to the contrary After

calculating potential growth actual daily growth of each organ is determined within an hierarchical routine where assimilated C becomes increasingly limiting in relation to potential growth The structure of this routine is briefly illustrated below

1 If CARBO gt GROTUB CARBO is updated as CARBO = CARBO-GROTUB to reflect the priority given to tuber growth

2 If CARBO or CARBO+RVCHO gt GROLF+GROSTM+GRORT all organs grow at the estimated rate The reserve carbohydrate pool is adjusted accordingly

3 If CARBO+RVCHO lt GROLF+GROSTM+GRORT all reserve carbohydrate is used and the growth of leaves stens and roots is reduced by a growth reduction factor (GRF)

GRF = (CARBO+RVCHO)(GROLF+GROSTM+GRORT) [17]

13


4 If CARBO+RVCHO lt GROTUB tuber growth is reduced by (CARBO+RVCHO)GROTUB and growth of all other organs is set to zero

Stress Factors

Soil Water Deficit Factor (SWDF) The effects of soil water deficit (SWD) on potato were reviewed by Van Loon (1981) and include reduced expansion growth and photosynthetic rates inceased allocation of biomshyass to tubers and increased rate of phenological development The manner ii which these effects are simulated in SUBSTOR-Potato were discussed previously Two generalized SWD factors are used in this model 3WDF1 and SWDF2 simulate the effects of SWD on photosynthesis and growth respectively SWDF2 is also used in various capacities as a modifier for developmental rates and partitioning of biomass

Leaf and soil water potentials are not directly estimated in the model Thus SWDF1 and SWDF2 can be viewed as the relative deficiency between potential water uptake by roots and transpiration from the leaf surface Both deficit factors are calculated as a ratio of total root water uptake potential (TRWU) and transpiration (EP1) TRWU is a function of rooting depth root length density and soil water content and distribution The greater sensitivity of leaf expansion to SWD was demonstrated by Munns and Pearson (1974) and Jeffries and MacKerron (1987 1989) attributable to the loss of leaf cell turgor under deficit conditions The use of linear SWDFs is supported by Jeffries (1989) who found that leaf extension rate was directly proportional to leaf water potential and leaf turgor In addition Vos and Oyarzain (1987) observed a linear relationship between leaf water potential and photosynthetic rate

Nitrogen Deficit Factor (NDEF subroutine NFACTO) The N concentration of potato vines is a much more sensitive indicator to transient changes in N fertility than is the concentration in either roots or tubers Vine N concentration also generally declines with advancing maturity even under N sufficient conditions For these reasons the estimation of N deficiency or excess in SUBSTOR-Potato is based solely on vine N concentration

Critical N concentration in the vines or tops (TCNP) is the concentration required to maintain maximum growth and photosynthetic rates Minimum N concentration (TMNC) is the concentration below which growth and photosynthesis cease The values for TCNP are approximated from Saffigna and Keeney (1977) and Greenwood et al (1985) In SUBSTOR-Potato the N concentration in roots and tubers (that have a critical N concentrashytion TUBCNP of 14 percent regardless of growth stage) is maintained at or slightly above

14


the respective critical values reflecting the lack of fertility effects on these organs (eg Carter and Bosma 1974 Saffigna and Keeney 1977 Kleinkopf et al 1981)

The extent of N deficiency (NFAC) is measured on a linear scale relative to the minimum and critical concentrations by

NFAC =(TCNP-TANC)(TCNP-TMNC) [18]

where TANC is the actual N concentration in the vines NFAC is then used to calculate NDEF1 and NDEF2 for photosynthesis and growth respectively

NDEF1 = NFAC [19] NDEF2 = 095NFAC

Under conditions of excess N NFAC is allowed to exceed 10 and is used to delay developshyment of the plant

Performance of Substor-Potato

The validation data set for SUBSTOR-Potato (Table 3) includes a wide range of geographishycal regions cultivars and management intensities (eg irrigation N fertilization) Because of the diversity of these data we have intentionally limited our presentation of model validation in several ways First we chose not to conduct a formal sensitivity analysisin our evaluation The inclusion of diverse data in the validation set seemed to us to make such analyses redundant For example within the validation set climatic condishytions range from cool temperatureshort photoperiod to high temperatureshortphotoperiod (both from Hawaii Manrique and Bartholomew 1991) to cool temperature long photoperiod (Scotland Jeffries and MacKerron 1987 1989) Likewise early intermeshydiate and late cultivars (eg Norchip Katahdin and Russett Burbank) were included in the validation set

The second limitation is the scope of validation specifically for plant stress factors Alshythough the validation set inherently addresses the effects of N and soil water stresses on develshyopment growth and yield we have not included validation statistics on plant-soil N (or water)balance This decision was based on the fact that most data sets did not include sufficient soil and plant N analyses to adequately evaluate the nitrogen subroutines in the model

15

i

Table 3 Descriptionofvalidationdata forSUBSTOR-Potato V2

Location Latitude Cultivar(s) Year(s) Reference(s)

Murrunbridge Australia 350 S Sebago 1970 1971 Sale (1973) Olinda HA (1097 m) 205 N Katahdin 1986 Manrique amp Bartholomew (1991)

Desiree Hamakuapoko HA (91 m) 206 N Katahdin 1986 Manrique amp Bartholomew (1991)

Desiree Haleakala HA (640 m) 230 N Katahdin 1986 Manrique amp Bartholomew (1991)

Desiree Ithaca NY Katahdin 1980 1981 1982 1985 1986 Ewing et al (1990) Kimberly ID 423 N Russet Burbank 1978 Ritchie (unpublished) Aberdeen ID 430 N Russet Burbank 1978 Ritchie (unpublished) Entrican MI 432 N Russet Burbank 1985 19861987 1988 Ritchie (unpublished) Hermiston OR 458 N Russet Burbank 1988 English (unpublished) Grand Forks ND 479 N Russet Burbank 1985 19861987 Ritchie (unpublished)

Norchip Invergowrie Scotland 565 N Maris Piper 1984 198519861987 Jeffries amp MacKerron (1987 1989)

En


To demonstrate the performance of SUBSTOR-Potato we have included two distinct approaches in the following discussion First the traditional approach showing the relashytionship between simulated and observed values using all available validation data And second (where appropriate) simulated versus observed values for a single growing season at a specific location (ie seasonal growth pattern)

Simulation of Tuber Initiation Date

Tuber initiation (TI) represents the critical phenological event of potato during the growing season and unlike grain crops for example is the only distinct phenological event after emergence Simulation of the timing of this event is made more difficult by the indetermishynate growth habit of some potato cultivars For these reasons we put considerable effort into developing a framework for simulating this event that would be useful across environshyments and cultivars Within our validation data set a strong linear relationship was apparshyent between simulated and observed time to TI (Figure 4) demonstrating the utility of our approach to simulating TI Despite unknown differences in seed piece physiological age SUBSTOR-Potato accurately simulated TI under conditions highly conducive for initiation (30 to 40 DAP) and under conditions that delayed initiation until mid-season (80 to 90 DAP) The intercept (a) of the regression of simulated on observed values was 1935

100 SIM = 1935 + 0726 (OBS) r = 0921

90 - Figure 4

8 Simulated ltAversus

0 70 - observedtime to tuber 0 -- A initiation

7 An= 41 so A a

- 40

30

- 11 line 20 I I I I I I

20 30 40 50 60 70 80 90 100

Observed time toTI (DAP)

17


indicating that the initiation occurred earlier than the model simulated This was expected because the model does not simulate the discrete lag phase of tuber growth that occurs immediately after initiation

The general approach we have taken to simulate phenological development incorporating both temperature and photoperiod effects is not unique to SUBSTOR-Potato or potato simulation models in general Hammer et al (1989) analyzed grain sorghum (Sorghum bicolor (L) Moench) development in diverse environments and developed functions for temperature and photoperiod response very similar to those reported here They also recognized that cultivars (or cultivar groups) differ in their response to these factors Russell and Stuber (1985) demonstrated that tassel initiation in maize (Zea mays L) inshyvolved an interaction of temperature and photoperiod effects and that the effect of photoshyperiod was minimal after tassel initiation Similar relationships have been shown for the rate of development by soybean [Glycine max (L) Merr] (Major et al 1975) The integration of climatic effects the extrapolation of linear tuber bulking to define tuber initiation and the recognition of cultivar differences which are features unique to this model represent a new approach to modeling potato development Compared to previous models this approach may be preferable for several reasons First compared to simulation based solely on temperature this approach takes into account the obvious effects of photoshyperiod on delaying or hastening TI Second although we realize that refinement of the cultivar-specific genetic coefficients (Table 2) are likely this approach recognizes that cultivars are distinctly different in their response to climate And third compared to mechanistic models this approach greatly simplifies the simulation of TI This may change as the physiological basis and control factors for TI are clarified

Simulation of Leaf Growth

Carbon assimilation by potato is directly related to the ability of the plant to intercept solar radiation which is in turn a function of photosynthetically active leaf area SUBSTOR-Potato does not simulate the development of individual leaves as in Ng and Loomis (1984) but rather the development of the entire canopy The models ability to simulate leaf area expansion or leaf biomass was evaluated in two ways First we compared simulated and observed maximum LAI values (Figure 5) Essentially this evaluates the model equations for pctential leaf growth given prevailing climatic and fertility conditions Compared to observed values the model performance was poorer than that for tuber initiation with a correlation coefficient (r) of 047 For maximum LAI between 4 and 6 the model tended to overestimate leaf area Most likely this was due to the lack of disease and(or) insect defoshyliation subroutines within the model which would serve to constrain leaf area The potershytial for coupling pathogen submodels with CERES-type crop models was discussed by

18

2


8

SIM = 182 + 0708 (OBS) r = 0473 A Figure5

Simulated 6 versusob-

A served maxinum leaf

4 area Index -a a (LAI) during

growing season

n =32

A - 11 line Si I I I I I

0 1 2 3 4 5 6 7 8

Observed Maximum LAI (M2 M-2)

Rickman and Klepper (1991) and should represent a major goal of plant simulation modelshying At very low observed maximum LAI the model underestimated leaf area suggesting that the model may be overly sensitive to severe environmental conditions (eg drought N stress) As pointed out by Greenwood et al (1985b) and Jones and Kiniry (1986) crop growth simulation is more difficult under extreme conditions because small differences in initial soil water and soil N levels have disproportionally dramatic effects on simulated plant growth The designation of tubers as having fii-st priority on assimilated C may also play a role under extreme conditions that result in less leaf area being produced this priority may cause leaf growth to cease completely

To further evaluate discrepancies in the simulation of leaf growth by the model we present seasonal leaf growth patterns for three location-years Oregon 1988 Scotland 1984 and North Dakota 1987 These data represent very high moderately high and low tuber yield potentials Simulated leaf growth under long photoperiod and irrigated conditions of Oregon was nearly identical to the observed leaf growth pattern (Figure 6A) This indicates that the functions to estimate leaf growth in the nearly complete absence of plant stress were appropriate Under conditions slightly less favorable for canopy growth (Scotland) simulation of initial leaf development followed the growth patterns observed i the field

-1(Figure 6B) However observed leaf growth essentially ceased at about 40 g plant while

19


6000

5250

4500

Figure 6A Simulated (line) versus observed

3750

3000

2250

1500

[(symbols)

seasonal leaf growth for Russet Burbank Oregon 1988

750

000 100 121 143 165 187 209 291 253 275

Day of the Year

1simulated growth continued to more than 50 g plant As mentioned previously the model may continue to simulate leaf growth because there is no simulation of disease or

6000

Figure 6B5250 Simulated

4500 (line) versus observed

3750 - (symbols) [3 seasonal leaf

300 growth for 2250 Marls Piper

0 0a Scotland

1500 1984

750

000 100 121 142 163 185 206 227 248 270

Day of the Year

20


2500

217 Figure 6C Simulated

1875 0 (line) versus 1 15s62

observed (symbols)

12501250o1 seasonal leaf 3 growth for

9-37 -Russet

62562 Burbank North Dakota

312 0 1985

000c 100 120 140 160 180 200 220 240 260

Day of the Year

insect effects in the model Much smaller plants were observed in North Dakota (Figure 6C) due to the lack of irrigation Tile model was successful in estimating the maximum canopy size (about 20 g plant-) but the time that maximum canopy size occurred was approximately 50 D prior to that observed in the field The cessation of simulated leaf growth at Julian date 190 corresponds exactly to when tuber initiation occurred providing evidence that the priority for assimilated C may be too rigid

Simulation of Tuber Yield

Kiniry and Jones (1986) discuss the integration of numerous model processes in simulating maize grain yield within the CERES-Maize model They noted that simulated grain yield was affected by virtually every process simulated by the model The same can be said of tuber yield simulation by SUBSTOR-Potato Tuber yield is influenced by rate of developshyment intercepted radiation and use efficiency biomass partitioning fertility and soil water status and other factors Tuber yield also represents the variable of economic importance Thus accurate simulation of tuber yield is essential We found that the simulation of tuber yield by our model was quite accurate (Figure 7) over a range of two or more than 20 Mg DM ha 1 The intercept (040) and slope (0958) and r value (0897) of the predicted versus observed regression indicates that the model was not biased across this ten-fold range in

21


24

SIM = 0400 + 0958 (OBS) r = 0897 A A Figure 720 Simulated

Aversus

16 shy observed A A tuber yield

12 n=54

AA AA

8AAAamp

A A

4 AA

AI -11 line0 I I I 1 I

0 4 8 12 16 20 24

Observed Tuber Yield (Mg DM ha l)

tuber yield Except for two outlying data points where yield was greatly underestimated there was no systematic under- or overestimation of tuber yield Seasonal tuber growth for

120 -

Figure 8A105 -Simulated

90 (line) versus S[observed

75- (symbols) 0 seasonal60

60 tuber growth 0s for Russet

Burbank 30 Oregon 1988

15

00

100 121 143 165 187 209 231 253 275

Day of the Year

22

105


120-

Figure 8B a Simulated

^9 a(line) versus 3 13 observed

075 (symbols) seasonal tuber growth

45 13 for Marls 4Piper Scotshy

land 1984 15 0

0 0 100 121 143 165 187 209 231 253 275

Day of the Year

the same locations described previously for leaf growth also indicates that the rate of tuber growth was accurately simulated by SUBSTOR-Potato (Figure 8ABC) This is true even where the simulation of leaf growth was not successful (eg North Dakota data set)

180-

Figure 8C 157 - Simulated

(line) versus 135

observed 112shy (symbols)

seasonal 90 tuber growth

S676 for RussetBurbank

45 North Dakota

1985

0 100 120 140 160 180 200 220 240 280

Day of the Year

22

23


Comparing the performance of our model with previous models for predicting tuber yield is difficult Validation of many of the previous potato models consisted of seasonal growth within a single growing season or several growing seasons (eg Fishman et al 1984 Ingram and McCloud 1984 Ng and Loomis 1984 Ewing et al 1990) These models validate different approaches to modeling potato growth and development at the empirical or physiological levels but give little indication of the response of cultivars to changing climate or management Other models (Ng and Loomis 1984 MacKerron 1985 Greenshywood et al 1985a b) conduct formal sensitivity analyses making independent changes in key input variables to identify factors controlling growth and development As discussed at the beginning of this section we did not conduct a sensitivity analysis because of the diverse validation data set

Jeffries et al (1991) in their validation of a model originally developed by MacKerron and Waister (1985) compared simulated versus observed yield for three growing seasons with different levels of soil water stress Their model accurately (r2 = 088) simulated yield response to soil water stress but was limited to a single cultivar (Maris Piper) at one locashytion Greenwood et al (1985b) evaluated the performance of a model of potato growth and N status and found very good agreement between simulated and observed tuber yields However their validation data set included yield measurements from serial harvests which may result in auto correlation from one harvest to the next All experiments inshycluded were conducted on three different soils but under similar climatic conditions Yield simulation by other CERES-type crop models like CERES-Maize and SOYGRO include validation data from different cimates and cultivars The validation of these models was similar to ours for SUBSTOR-Potato

Simulation of End-of-season Biomass

End-of-season biomass (consisting of tubers remaining vine biomass and roots) is the summation of biomass accumulation and biomass loss due to senescence or root turnover In our model it is essentially a measurement at any time which may be after desiccation of vine material Accurate simulation of this variable is important for biomass and N cycling within the potato crop We found that our model simulated end-of-season biomass about as well as tuber yield (Figure 9) This is not surprising because most of the biomass remainshying at the end of the growing season is in the tubers Except for two outlying underestimated biomass yields total end-of-season biomass tendrd to be slightly overestimated by the model The overestimation of haulm biomass discussed earlier due to the lack of pathogen- or pest-induced defoliation in the model probably contributes to this overestimation

24


30

SIM = -0959 + 113(OBS) r =0905 A

25 a

Figure 9 Simulated

A20versus b0 20

Aobserved

end-ofshy15 -

season E

-1 6

amp biomass

yield n =40

11line0I I I I I

0 5 10 15 20 25 30

1Observed Biomass Yield (Mg DM ha )

Conclusions

In this paper we have discussed the development and initial validation of a new shnulashytion model for potato growth and development This model simulates the phenological development of the potato crop including a new approach to incorporating temperature and photoperiod effects on tuber initiation It also simulates growth using a capacity model for carbon fixation constrained by radiation high temperatures nitrogen deficit and soil water stress

The performance of this model for numerou- cultivars grown in diverse climates was similar to that of other CERES-type crop models but comparison to previous potato modshyels is difficult Previous models of potato growth are generally specific to cultivar environshyment or both and validation in some cases consists of only seasonal growth patterns The SUBSTOR-Potato model validation presented may be considered preliminary but the model has great potential for simulating potato growth and evaluating potential changes in management in many regions

25


References

Allen EJ and RK Scott 1980 An analysis of growth of the potato crop 1AgricSci-Camb 94583-606

Ben Khedher M and EE Ewing 1985 Growth analysis of eleven potato cultivars grown in the greenhouse under long photoperiods with and without heat stress Amer Potato162537-554

Carter JM and SM Bosma 1974 Effect of fertilizer aid irrigation on nitrate-nitrogen and total nitrogen in potato tubers Agron 166263-266

Epstein E 1966 Effect of temperature at different growth stages on growth and developshyment of potato plants Agron J 58169-177

Ewing EE 1981 Heat stress and the tuberization stimulus Amer Potato15831-49

Ewing EE WD Heym EJ Batutis RG Snyder M Ben Khedher KP Sandlan and AD Turner 1990 Modifications to the simulation model POTATO for use in New York Agric Syst 33173-192

Fishman S H Talpaz M Dinar M Levy Y Arazi Y Rozman and S Varshavsky 1984 A phenomenological model of dry matter partitioning among potato organs for simulation of potato growth Agric syste 14159-169

Greenwood DJ JJ Neeteson and A Draycott 1985a Response of potatoes to N fertilizer quantitative relations for components of growth PlantSoil 85163-183

Greenwood DJ JJ Neeteson and A Draycott 1985b Response of potatoes to N fertilizer dynamic model Plant Soil 85185-203

Hammer GL RL Vanderlip G Gibson LJ Wade RG Henzell DR Younger J Warshyren and AB Dale 1989 Genotype-by-environment interaction in grain sorghum I effects of temperature and photoperiod on ontogeny Crop Sci 29376-384

Hartz TK and FD Moore III 1978 Prediction of potato yield using temperature and insolation data Amer PotatoJ 55431-436

26


IBSNAT 1986 TechnicalReport 5 Documentationfor IBSNAT crop model inputand outputfiles Version 10 Dept of Agron Soil Sci College of Trop Agric and Human Resources Univ Hawaii

Ingram KT and DE McCloud 1984 Simulation of potato crop growth and development Crop Sci 2421-27

Iritani WM 1963 The effect of summer temperature in Idaho on yield of Russett Burbank potatoes Amer Potato14047-52

Jeffries JA 1989 Water-stress and leaf growth in field growth of crops of potato (Solanum tuberosum L) 1Expt Bot 401375-1381

Jeffries JA and DKL MacKerron 1987 Aspects of the physiological basis of cultivar differences in yield of potato under droughted and irrigated conditions PotatoRes 30201-207

Jeffries JA and DKL MacKerron 1989 Radiation interception and growth of irrigated and droughted potato (Solanum tuberosum)FieldCrop Res 22101-112

Johnson KB SB Johnson and PS Teng 1986 Development of a simple potato growthmodel for use in crop-pest management Agric Syst 19189-209

Jones CA and JR Kiniry (eds) 1986 CERES-Maizea simulationmodel of maizegrowthand development Texas A ampM Univ Press College Station TX

Kleinkopf GE DT Westermann and RB Dwelle 1981 Dry matter production and nitrogen utilization by six potato cultivars Agron 173799-803

MacKerron OKL 1985 A simple model of potato growth and yield Part IIvalidation and external sensitive Agric Forest Meteor 34285-300

MacKerron DKL and JA Jeffries 1986 The influence of early moisture stress on tuber numbers in potato PotatoRes 29299-321

MacKerron DKL and PD Waister 1985 A simple model of potato growth and yieldPart IModel development and sensitivity analysis AgricForestMeteor 34241-252

27


Major DJ DR Johnson JW Tanner and IC Anderson 1975 Effects of daylength and temperature on soybean development Crop Sci 15174-179

Manrique LA and DP Bartholomew 1991 Growth and yield performance of potato grown at three elevations in Hawaii IIDry matter production and efficiency of partitioning CropSci 31367-372-

Manrique LA and T Hodges 1989 Estimation of tuber initiation in potatoes grown in tropical environments based on different methods of computing thermal time Amer Potato166425-436

Manrique LA JR Kiniry T Hodges and DS Axness 1991 Dry matter production and radiation interception of potato CropSci 311044-1049

Marinus J and KBA Bodleander 1975 Response of some potato varieties to temperature PotatoRes18189-204

Moorby J and FL Milthorpe 1975 Potato pp 225-257 In LJEvans (ed) Crop physiolshyogy Cambridge Univ Press London

Munns R and CJ Pearson 1974 Effect of water deficit on translocation of carbohydrate in Solanum tuberosumAust 1Pl Phys 1529-537

Ng E and RS Loomis 1984 Simulation of growth and yield of the potato crop Simulashytion MonographsPudoc Wageningen

Prange RK KB McRae DJ Midinore and R Deng 1990 Reduction in potato growth at high temperature role of photosynthesis and dark respiration Amer Potato167357-369

Rasco ET Jr RL Plaisted and EE Ewing 1980 Photoperiod response and earliness of S tuberosum spp Andigena after six cycles of recurrent selection for adaptation to long days Amer Potatr157435-448

Rickman RW and BKlepper 1991 Envronmentally driven cereal crop growth models Ann Rev Phytopath29-61-380

Ritchie JT TS Griffin and BS Johnson 1992 SUBSTOR - a functional model of potato growth development and yield SimulationMonographsPUDOC Wageningen The Netherlands In press

28


Russell WK and CW Stuber 1985 Genotype x photoperiod and genotype x temperature interactions for maturity in maize CropSci 25152-158

Saffigna PG and DR Keeney 1977 Nitrogen and chloride uptake by irrigated Russet Burbank potatoes Agron 169258-264

Sale PJM 1973 Productivity of vegetable crops in a region of high solar input IIyields and efficiencies of water use and energy Aust JAgric Res 24751-762

Sands PJ C Hackett and HA Nix 1979 A model of the development and bulking of potatoes (Solanumtuberosum L) Iderivation from well-managed field crops Field Crop Res 2309-331

Slater JW 1968 The effect of night temperature on tuber initiation of the potato Eur Potato11114-22

Snyder RG and EE Ewing 1989 Interactive effects of temperature photoperiod and cultivar on tuberization of potato cuttings HortSci 24336-338

Van der Zaag DE and CD Van Loon 1987 Effect of physiological age on growth vigour of seed potatoes of two cultivars 5Review of literature and integration of some experimental results Potato Res 30451-472

Van Loon CD 1981 The effect of water stress on potato growth development and yield Amer PotatoJ5851-69

Vos J and PJ Oyarzain 1987 Photosynthesis and stomatal conductance of potato leaves shyeffects of leaf age irradiance and leaf water potential Photosyn Res 11253-264

Wheeler RM and TW Tibbetts 1986 Utilization of potatoes for life support systems in space ICultivar-photoperiod interactions Amer Potato163315-323

Yamaguchi M H Timm and AR Spurr 1964 Effect of soil temperature on growth and nutrition of potato plants and tuberization composition and periderm structure of tubers Amer Soc Hort Sci J84412-423

29

Published for distribution by the IBSNAT Project

Michigan State University Department of Crop and Soil Sciences East Lansinamp Michigan 48824-1325

and

University of Hawaii College of Tropical Agriculture ampHuman Resources Department of Agronomy and Soil Science Honolulu Hawaii 96822

Support for the IBSNAT Project isprovided through a cooperative agreement (DAN-4054-A-o0-7081-00) between the USAgency for International Development and the University of Hawaii All reported opinions conclusions and recommenshydations are those of the authors and not those of the funding agency or the United States government

Corect citation IBSNAT 1993 Research Report Series 02 A Simulation Model for Potato Growth and DevelopmentSUBSTOR - Potato Version 20 Dept of Agronomy and Soil Science College of TropicalAgriculture and Human Resources Univ of Hawaii Honolulu HI

IBSNAT Research Report Series 02--593 (500)










Introduction





Model Development




2



Variable name Units




RTFTI none

SEEDAV g plant 1




RTFSOIL none

RTFVINE none

XTEMP degree C


CARBO g plant- d- 1

DDEADLF g plant-1

DEVEFF none

Description






















priority





which growth ceases


100

o0O Figure1




(RTFSOIL)

100

OJO

= 060

040

020

000 A I i I I I

0 5 10 15 20 25 30 35 40





















Figure 2









temperatures)

calculated as







RDLFTI = (10-P2)+000694P2(240-PHPER) 2 [51








0daylength relative



6 9 12 is 16 21 24

D-0--ao-


coefficient)

Photoperiod (hr)

9




C171 = ITII [7]








10










1









12














13



Stress Factors





14











15

i








En





100 SIM = 1935 + 0726 (OBS) r = 0921

90 - Figure 4



7 An= 41 so A a

- 40

30


20 30 40 50 60 70 80 90 100


17






18

2


8

SIM = 182 + 0708 (OBS) r = 0473 A Figure5




growing season

n =32


0 1 2 3 4 5 6 7 8





19


6000

5250

4500


3750

3000

2250

1500

[(symbols)


750

000 100 121 143 165 187 209 291 253 275

Day of the Year


6000





0 0a Scotland

1500 1984

750

000 100 121 142 163 185 206 227 248 270

Day of the Year

20


2500


1875 0 (line) versus 1 15s62

observed (symbols)


9-37 -Russet


312 0 1985

000c 100 120 140 160 180 200 220 240 260

Day of the Year




21


24


Aversus


12 n=54

AA AA

8AAAamp

A A

4 AA


0 4 8 12 16 20 24



120 -






15

00

100 121 143 165 187 209 231 253 275

Day of the Year

22

105


120-





land 1984 15 0

0 0 100 121 143 165 187 209 231 253 275

Day of the Year


180-


(line) versus 135




45 North Dakota

1985

0 100 120 140 160 180 200 220 240 280

Day of the Year

22

23






24


30

SIM = -0959 + 113(OBS) r =0905 A

25 a

Figure 9 Simulated

A20versus b0 20

Aobserved

end-ofshy15 -

season E

-1 6

amp biomass

yield n =40

11line0I I I I I

0 5 10 15 20 25 30


Conclusions



25


References












26














27














28













29










Introduction





Model Development




2



Variable name Units




RTFTI none

SEEDAV g plant 1




RTFSOIL none

RTFVINE none

XTEMP degree C


CARBO g plant- d- 1

DDEADLF g plant-1

DEVEFF none

Description






















priority





which growth ceases


100

o0O Figure1




(RTFSOIL)

100

OJO

= 060

040

020

000 A I i I I I

0 5 10 15 20 25 30 35 40





















Figure 2









temperatures)

calculated as







RDLFTI = (10-P2)+000694P2(240-PHPER) 2 [51








0daylength relative



6 9 12 is 16 21 24

D-0--ao-


coefficient)

Photoperiod (hr)

9




C171 = ITII [7]








10










1









12














13



Stress Factors





14











15

i








En





100 SIM = 1935 + 0726 (OBS) r = 0921

90 - Figure 4



7 An= 41 so A a

- 40

30


20 30 40 50 60 70 80 90 100


17






18

2


8

SIM = 182 + 0708 (OBS) r = 0473 A Figure5




growing season

n =32


0 1 2 3 4 5 6 7 8





19


6000

5250

4500


3750

3000

2250

1500

[(symbols)


750

000 100 121 143 165 187 209 291 253 275

Day of the Year


6000





0 0a Scotland

1500 1984

750

000 100 121 142 163 185 206 227 248 270

Day of the Year

20


2500


1875 0 (line) versus 1 15s62

observed (symbols)


9-37 -Russet


312 0 1985

000c 100 120 140 160 180 200 220 240 260

Day of the Year




21


24


Aversus


12 n=54

AA AA

8AAAamp

A A

4 AA


0 4 8 12 16 20 24



120 -






15

00

100 121 143 165 187 209 231 253 275

Day of the Year

22

105


120-





land 1984 15 0

0 0 100 121 143 165 187 209 231 253 275

Day of the Year


180-


(line) versus 135




45 North Dakota

1985

0 100 120 140 160 180 200 220 240 280

Day of the Year

22

23






24


30

SIM = -0959 + 113(OBS) r =0905 A

25 a

Figure 9 Simulated

A20versus b0 20

Aobserved

end-ofshy15 -

season E

-1 6

amp biomass

yield n =40

11line0I I I I I

0 5 10 15 20 25 30


Conclusions



25


References












26














27














28













29


Introduction





Model Development




2



Variable name Units




RTFTI none

SEEDAV g plant 1




RTFSOIL none

RTFVINE none

XTEMP degree C


CARBO g plant- d- 1

DDEADLF g plant-1

DEVEFF none

Description






















priority





which growth ceases


100

o0O Figure1




(RTFSOIL)

100

OJO

= 060

040

020

000 A I i I I I

0 5 10 15 20 25 30 35 40





















Figure 2









temperatures)

calculated as







RDLFTI = (10-P2)+000694P2(240-PHPER) 2 [51








0daylength relative



6 9 12 is 16 21 24

D-0--ao-


coefficient)

Photoperiod (hr)

9




C171 = ITII [7]








10










1









12














13



Stress Factors





14











15

i








En





100 SIM = 1935 + 0726 (OBS) r = 0921

90 - Figure 4



7 An= 41 so A a

- 40

30


20 30 40 50 60 70 80 90 100


17






18

2


8

SIM = 182 + 0708 (OBS) r = 0473 A Figure5




growing season

n =32


0 1 2 3 4 5 6 7 8





19


6000

5250

4500


3750

3000

2250

1500

[(symbols)


750

000 100 121 143 165 187 209 291 253 275

Day of the Year


6000





0 0a Scotland

1500 1984

750

000 100 121 142 163 185 206 227 248 270

Day of the Year

20


2500


1875 0 (line) versus 1 15s62

observed (symbols)


9-37 -Russet


312 0 1985

000c 100 120 140 160 180 200 220 240 260

Day of the Year




21


24


Aversus


12 n=54

AA AA

8AAAamp

A A

4 AA


0 4 8 12 16 20 24



120 -






15

00

100 121 143 165 187 209 231 253 275

Day of the Year

22

105


120-





land 1984 15 0

0 0 100 121 143 165 187 209 231 253 275

Day of the Year


180-


(line) versus 135




45 North Dakota

1985

0 100 120 140 160 180 200 220 240 280

Day of the Year

22

23






24


30

SIM = -0959 + 113(OBS) r =0905 A

25 a

Figure 9 Simulated

A20versus b0 20

Aobserved

end-ofshy15 -

season E

-1 6

amp biomass

yield n =40

11line0I I I I I

0 5 10 15 20 25 30


Conclusions



25


References












26














27














28













29



Model Development




2



Variable name Units




RTFTI none

SEEDAV g plant 1




RTFSOIL none

RTFVINE none

XTEMP degree C


CARBO g plant- d- 1

DDEADLF g plant-1

DEVEFF none

Description






















priority





which growth ceases


100

o0O Figure1




(RTFSOIL)

100

OJO

= 060

040

020

000 A I i I I I

0 5 10 15 20 25 30 35 40





















Figure 2









temperatures)

calculated as







RDLFTI = (10-P2)+000694P2(240-PHPER) 2 [51








0daylength relative



6 9 12 is 16 21 24

D-0--ao-


coefficient)

Photoperiod (hr)

9




C171 = ITII [7]








10










1









12














13



Stress Factors





14











15

i








En





100 SIM = 1935 + 0726 (OBS) r = 0921

90 - Figure 4



7 An= 41 so A a

- 40

30


20 30 40 50 60 70 80 90 100


17






18

2


8

SIM = 182 + 0708 (OBS) r = 0473 A Figure5




growing season

n =32


0 1 2 3 4 5 6 7 8





19


6000

5250

4500


3750

3000

2250

1500

[(symbols)


750

000 100 121 143 165 187 209 291 253 275

Day of the Year


6000





0 0a Scotland

1500 1984

750

000 100 121 142 163 185 206 227 248 270

Day of the Year

20


2500


1875 0 (line) versus 1 15s62

observed (symbols)


9-37 -Russet


312 0 1985

000c 100 120 140 160 180 200 220 240 260

Day of the Year




21


24


Aversus


12 n=54

AA AA

8AAAamp

A A

4 AA


0 4 8 12 16 20 24



120 -






15

00

100 121 143 165 187 209 231 253 275

Day of the Year

22

105


120-





land 1984 15 0

0 0 100 121 143 165 187 209 231 253 275

Day of the Year


180-


(line) versus 135




45 North Dakota

1985

0 100 120 140 160 180 200 220 240 280

Day of the Year

22

23






24


30

SIM = -0959 + 113(OBS) r =0905 A

25 a

Figure 9 Simulated

A20versus b0 20

Aobserved

end-ofshy15 -

season E

-1 6

amp biomass

yield n =40

11line0I I I I I

0 5 10 15 20 25 30


Conclusions



25


References












26














27














28













29



Variable name Units




RTFTI none

SEEDAV g plant 1




RTFSOIL none

RTFVINE none

XTEMP degree C


CARBO g plant- d- 1

DDEADLF g plant-1

DEVEFF none

Description






















priority





which growth ceases


100

o0O Figure1




(RTFSOIL)

100

OJO

= 060

040

020

000 A I i I I I

0 5 10 15 20 25 30 35 40





















Figure 2









temperatures)

calculated as







RDLFTI = (10-P2)+000694P2(240-PHPER) 2 [51








0daylength relative



6 9 12 is 16 21 24

D-0--ao-


coefficient)

Photoperiod (hr)

9




C171 = ITII [7]








10










1









12














13



Stress Factors





14











15

i








En





100 SIM = 1935 + 0726 (OBS) r = 0921

90 - Figure 4



7 An= 41 so A a

- 40

30


20 30 40 50 60 70 80 90 100


17






18

2


8

SIM = 182 + 0708 (OBS) r = 0473 A Figure5




growing season

n =32


0 1 2 3 4 5 6 7 8





19


6000

5250

4500


3750

3000

2250

1500

[(symbols)


750

000 100 121 143 165 187 209 291 253 275

Day of the Year


6000





0 0a Scotland

1500 1984

750

000 100 121 142 163 185 206 227 248 270

Day of the Year

20


2500


1875 0 (line) versus 1 15s62

observed (symbols)


9-37 -Russet


312 0 1985

000c 100 120 140 160 180 200 220 240 260

Day of the Year




21


24


Aversus


12 n=54

AA AA

8AAAamp

A A

4 AA


0 4 8 12 16 20 24



120 -






15

00

100 121 143 165 187 209 231 253 275

Day of the Year

22

105


120-





land 1984 15 0

0 0 100 121 143 165 187 209 231 253 275

Day of the Year


180-


(line) versus 135




45 North Dakota

1985

0 100 120 140 160 180 200 220 240 280

Day of the Year

22

23






24


30

SIM = -0959 + 113(OBS) r =0905 A

25 a

Figure 9 Simulated

A20versus b0 20

Aobserved

end-ofshy15 -

season E

-1 6

amp biomass

yield n =40

11line0I I I I I

0 5 10 15 20 25 30


Conclusions



25


References












26














27














28













29











priority





which growth ceases


100

o0O Figure1




(RTFSOIL)

100

OJO

= 060

040

020

000 A I i I I I

0 5 10 15 20 25 30 35 40





















Figure 2









temperatures)

calculated as







RDLFTI = (10-P2)+000694P2(240-PHPER) 2 [51








0daylength relative



6 9 12 is 16 21 24

D-0--ao-


coefficient)

Photoperiod (hr)

9




C171 = ITII [7]








10










1









12














13



Stress Factors





14











15

i








En





100 SIM = 1935 + 0726 (OBS) r = 0921

90 - Figure 4



7 An= 41 so A a

- 40

30


20 30 40 50 60 70 80 90 100


17






18

2


8

SIM = 182 + 0708 (OBS) r = 0473 A Figure5




growing season

n =32


0 1 2 3 4 5 6 7 8





19


6000

5250

4500


3750

3000

2250

1500

[(symbols)


750

000 100 121 143 165 187 209 291 253 275

Day of the Year


6000





0 0a Scotland

1500 1984

750

000 100 121 142 163 185 206 227 248 270

Day of the Year

20


2500


1875 0 (line) versus 1 15s62

observed (symbols)


9-37 -Russet


312 0 1985

000c 100 120 140 160 180 200 220 240 260

Day of the Year




21


24


Aversus


12 n=54

AA AA

8AAAamp

A A

4 AA


0 4 8 12 16 20 24



120 -






15

00

100 121 143 165 187 209 231 253 275

Day of the Year

22

105


120-





land 1984 15 0

0 0 100 121 143 165 187 209 231 253 275

Day of the Year


180-


(line) versus 135




45 North Dakota

1985

0 100 120 140 160 180 200 220 240 280

Day of the Year

22

23






24


30

SIM = -0959 + 113(OBS) r =0905 A

25 a

Figure 9 Simulated

A20versus b0 20

Aobserved

end-ofshy15 -

season E

-1 6

amp biomass

yield n =40

11line0I I I I I

0 5 10 15 20 25 30


Conclusions



25


References












26














27














28













29


100

o0O Figure1




(RTFSOIL)

100

OJO

= 060

040

020

000 A I i I I I

0 5 10 15 20 25 30 35 40





















Figure 2









temperatures)

calculated as







RDLFTI = (10-P2)+000694P2(240-PHPER) 2 [51








0daylength relative



6 9 12 is 16 21 24

D-0--ao-


coefficient)

Photoperiod (hr)

9




C171 = ITII [7]








10










1









12














13



Stress Factors





14











15

i








En





100 SIM = 1935 + 0726 (OBS) r = 0921

90 - Figure 4



7 An= 41 so A a

- 40

30


20 30 40 50 60 70 80 90 100


17






18

2


8

SIM = 182 + 0708 (OBS) r = 0473 A Figure5




growing season

n =32


0 1 2 3 4 5 6 7 8





19


6000

5250

4500


3750

3000

2250

1500

[(symbols)


750

000 100 121 143 165 187 209 291 253 275

Day of the Year


6000





0 0a Scotland

1500 1984

750

000 100 121 142 163 185 206 227 248 270

Day of the Year

20


2500


1875 0 (line) versus 1 15s62

observed (symbols)


9-37 -Russet


312 0 1985

000c 100 120 140 160 180 200 220 240 260

Day of the Year




21


24


Aversus


12 n=54

AA AA

8AAAamp

A A

4 AA


0 4 8 12 16 20 24



120 -






15

00

100 121 143 165 187 209 231 253 275

Day of the Year

22

105


120-





land 1984 15 0

0 0 100 121 143 165 187 209 231 253 275

Day of the Year


180-


(line) versus 135




45 North Dakota

1985

0 100 120 140 160 180 200 220 240 280

Day of the Year

22

23






24


30

SIM = -0959 + 113(OBS) r =0905 A

25 a

Figure 9 Simulated

A20versus b0 20

Aobserved

end-ofshy15 -

season E

-1 6

amp biomass

yield n =40

11line0I I I I I

0 5 10 15 20 25 30


Conclusions



25


References












26














27














28













29

















Figure 2









temperatures)

calculated as







RDLFTI = (10-P2)+000694P2(240-PHPER) 2 [51








0daylength relative



6 9 12 is 16 21 24

D-0--ao-


coefficient)

Photoperiod (hr)

9




C171 = ITII [7]








10










1









12














13



Stress Factors





14











15

i








En





100 SIM = 1935 + 0726 (OBS) r = 0921

90 - Figure 4



7 An= 41 so A a

- 40

30


20 30 40 50 60 70 80 90 100


17






18

2


8

SIM = 182 + 0708 (OBS) r = 0473 A Figure5




growing season

n =32


0 1 2 3 4 5 6 7 8





19


6000

5250

4500


3750

3000

2250

1500

[(symbols)


750

000 100 121 143 165 187 209 291 253 275

Day of the Year


6000





0 0a Scotland

1500 1984

750

000 100 121 142 163 185 206 227 248 270

Day of the Year

20


2500


1875 0 (line) versus 1 15s62

observed (symbols)


9-37 -Russet


312 0 1985

000c 100 120 140 160 180 200 220 240 260

Day of the Year




21


24


Aversus


12 n=54

AA AA

8AAAamp

A A

4 AA


0 4 8 12 16 20 24



120 -






15

00

100 121 143 165 187 209 231 253 275

Day of the Year

22

105


120-





land 1984 15 0

0 0 100 121 143 165 187 209 231 253 275

Day of the Year


180-


(line) versus 135




45 North Dakota

1985

0 100 120 140 160 180 200 220 240 280

Day of the Year

22

23






24


30

SIM = -0959 + 113(OBS) r =0905 A

25 a

Figure 9 Simulated

A20versus b0 20

Aobserved

end-ofshy15 -

season E

-1 6

amp biomass

yield n =40

11line0I I I I I

0 5 10 15 20 25 30


Conclusions



25


References












26














27














28













29









Figure 2









temperatures)

calculated as







RDLFTI = (10-P2)+000694P2(240-PHPER) 2 [51








0daylength relative



6 9 12 is 16 21 24

D-0--ao-


coefficient)

Photoperiod (hr)

9




C171 = ITII [7]








10










1









12














13



Stress Factors





14











15

i








En





100 SIM = 1935 + 0726 (OBS) r = 0921

90 - Figure 4



7 An= 41 so A a

- 40

30


20 30 40 50 60 70 80 90 100


17






18

2


8

SIM = 182 + 0708 (OBS) r = 0473 A Figure5




growing season

n =32


0 1 2 3 4 5 6 7 8





19


6000

5250

4500


3750

3000

2250

1500

[(symbols)


750

000 100 121 143 165 187 209 291 253 275

Day of the Year


6000





0 0a Scotland

1500 1984

750

000 100 121 142 163 185 206 227 248 270

Day of the Year

20


2500


1875 0 (line) versus 1 15s62

observed (symbols)


9-37 -Russet


312 0 1985

000c 100 120 140 160 180 200 220 240 260

Day of the Year




21


24


Aversus


12 n=54

AA AA

8AAAamp

A A

4 AA


0 4 8 12 16 20 24



120 -






15

00

100 121 143 165 187 209 231 253 275

Day of the Year

22

105


120-





land 1984 15 0

0 0 100 121 143 165 187 209 231 253 275

Day of the Year


180-


(line) versus 135




45 North Dakota

1985

0 100 120 140 160 180 200 220 240 280

Day of the Year

22

23






24


30

SIM = -0959 + 113(OBS) r =0905 A

25 a

Figure 9 Simulated

A20versus b0 20

Aobserved

end-ofshy15 -

season E

-1 6

amp biomass

yield n =40

11line0I I I I I

0 5 10 15 20 25 30


Conclusions



25


References












26














27














28













29


Figure 2









temperatures)

calculated as







RDLFTI = (10-P2)+000694P2(240-PHPER) 2 [51








0daylength relative



6 9 12 is 16 21 24

D-0--ao-


coefficient)

Photoperiod (hr)

9




C171 = ITII [7]








10










1









12














13



Stress Factors





14











15

i








En





100 SIM = 1935 + 0726 (OBS) r = 0921

90 - Figure 4



7 An= 41 so A a

- 40

30


20 30 40 50 60 70 80 90 100


17






18

2


8

SIM = 182 + 0708 (OBS) r = 0473 A Figure5




growing season

n =32


0 1 2 3 4 5 6 7 8





19


6000

5250

4500


3750

3000

2250

1500

[(symbols)


750

000 100 121 143 165 187 209 291 253 275

Day of the Year


6000





0 0a Scotland

1500 1984

750

000 100 121 142 163 185 206 227 248 270

Day of the Year

20


2500


1875 0 (line) versus 1 15s62

observed (symbols)


9-37 -Russet


312 0 1985

000c 100 120 140 160 180 200 220 240 260

Day of the Year




21


24


Aversus


12 n=54

AA AA

8AAAamp

A A

4 AA


0 4 8 12 16 20 24



120 -






15

00

100 121 143 165 187 209 231 253 275

Day of the Year

22

105


120-





land 1984 15 0

0 0 100 121 143 165 187 209 231 253 275

Day of the Year


180-


(line) versus 135




45 North Dakota

1985

0 100 120 140 160 180 200 220 240 280

Day of the Year

22

23






24


30

SIM = -0959 + 113(OBS) r =0905 A

25 a

Figure 9 Simulated

A20versus b0 20

Aobserved

end-ofshy15 -

season E

-1 6

amp biomass

yield n =40

11line0I I I I I

0 5 10 15 20 25 30


Conclusions



25


References












26














27














28













29



RDLFTI = (10-P2)+000694P2(240-PHPER) 2 [51








0daylength relative



6 9 12 is 16 21 24

D-0--ao-


coefficient)

Photoperiod (hr)

9




C171 = ITII [7]








10










1









12














13



Stress Factors





14











15

i








En





100 SIM = 1935 + 0726 (OBS) r = 0921

90 - Figure 4



7 An= 41 so A a

- 40

30


20 30 40 50 60 70 80 90 100


17






18

2


8

SIM = 182 + 0708 (OBS) r = 0473 A Figure5




growing season

n =32


0 1 2 3 4 5 6 7 8





19


6000

5250

4500


3750

3000

2250

1500

[(symbols)


750

000 100 121 143 165 187 209 291 253 275

Day of the Year


6000





0 0a Scotland

1500 1984

750

000 100 121 142 163 185 206 227 248 270

Day of the Year

20


2500


1875 0 (line) versus 1 15s62

observed (symbols)


9-37 -Russet


312 0 1985

000c 100 120 140 160 180 200 220 240 260

Day of the Year




21


24


Aversus


12 n=54

AA AA

8AAAamp

A A

4 AA


0 4 8 12 16 20 24



120 -






15

00

100 121 143 165 187 209 231 253 275

Day of the Year

22

105


120-





land 1984 15 0

0 0 100 121 143 165 187 209 231 253 275

Day of the Year


180-


(line) versus 135




45 North Dakota

1985

0 100 120 140 160 180 200 220 240 280

Day of the Year

22

23






24


30

SIM = -0959 + 113(OBS) r =0905 A

25 a

Figure 9 Simulated

A20versus b0 20

Aobserved

end-ofshy15 -

season E

-1 6

amp biomass

yield n =40

11line0I I I I I

0 5 10 15 20 25 30


Conclusions



25


References












26














27














28













29




C171 = ITII [7]








10










1









12














13



Stress Factors





14











15

i








En





100 SIM = 1935 + 0726 (OBS) r = 0921

90 - Figure 4



7 An= 41 so A a

- 40

30


20 30 40 50 60 70 80 90 100


17






18

2


8

SIM = 182 + 0708 (OBS) r = 0473 A Figure5




growing season

n =32


0 1 2 3 4 5 6 7 8





19


6000

5250

4500


3750

3000

2250

1500

[(symbols)


750

000 100 121 143 165 187 209 291 253 275

Day of the Year


6000





0 0a Scotland

1500 1984

750

000 100 121 142 163 185 206 227 248 270

Day of the Year

20


2500


1875 0 (line) versus 1 15s62

observed (symbols)


9-37 -Russet


312 0 1985

000c 100 120 140 160 180 200 220 240 260

Day of the Year




21


24


Aversus


12 n=54

AA AA

8AAAamp

A A

4 AA


0 4 8 12 16 20 24



120 -






15

00

100 121 143 165 187 209 231 253 275

Day of the Year

22

105


120-





land 1984 15 0

0 0 100 121 143 165 187 209 231 253 275

Day of the Year


180-


(line) versus 135




45 North Dakota

1985

0 100 120 140 160 180 200 220 240 280

Day of the Year

22

23






24


30

SIM = -0959 + 113(OBS) r =0905 A

25 a

Figure 9 Simulated

A20versus b0 20

Aobserved

end-ofshy15 -

season E

-1 6

amp biomass

yield n =40

11line0I I I I I

0 5 10 15 20 25 30


Conclusions



25


References












26














27














28













29










1









12














13



Stress Factors





14











15

i








En





100 SIM = 1935 + 0726 (OBS) r = 0921

90 - Figure 4



7 An= 41 so A a

- 40

30


20 30 40 50 60 70 80 90 100


17






18

2


8

SIM = 182 + 0708 (OBS) r = 0473 A Figure5




growing season

n =32


0 1 2 3 4 5 6 7 8





19


6000

5250

4500


3750

3000

2250

1500

[(symbols)


750

000 100 121 143 165 187 209 291 253 275

Day of the Year


6000





0 0a Scotland

1500 1984

750

000 100 121 142 163 185 206 227 248 270

Day of the Year

20


2500


1875 0 (line) versus 1 15s62

observed (symbols)


9-37 -Russet


312 0 1985

000c 100 120 140 160 180 200 220 240 260

Day of the Year




21


24


Aversus


12 n=54

AA AA

8AAAamp

A A

4 AA


0 4 8 12 16 20 24



120 -






15

00

100 121 143 165 187 209 231 253 275

Day of the Year

22

105


120-





land 1984 15 0

0 0 100 121 143 165 187 209 231 253 275

Day of the Year


180-


(line) versus 135




45 North Dakota

1985

0 100 120 140 160 180 200 220 240 280

Day of the Year

22

23






24


30

SIM = -0959 + 113(OBS) r =0905 A

25 a

Figure 9 Simulated

A20versus b0 20

Aobserved

end-ofshy15 -

season E

-1 6

amp biomass

yield n =40

11line0I I I I I

0 5 10 15 20 25 30


Conclusions



25


References












26














27














28













29









12














13



Stress Factors





14











15

i








En





100 SIM = 1935 + 0726 (OBS) r = 0921

90 - Figure 4



7 An= 41 so A a

- 40

30


20 30 40 50 60 70 80 90 100


17






18

2


8

SIM = 182 + 0708 (OBS) r = 0473 A Figure5




growing season

n =32


0 1 2 3 4 5 6 7 8





19


6000

5250

4500


3750

3000

2250

1500

[(symbols)


750

000 100 121 143 165 187 209 291 253 275

Day of the Year


6000





0 0a Scotland

1500 1984

750

000 100 121 142 163 185 206 227 248 270

Day of the Year

20


2500


1875 0 (line) versus 1 15s62

observed (symbols)


9-37 -Russet


312 0 1985

000c 100 120 140 160 180 200 220 240 260

Day of the Year




21


24


Aversus


12 n=54

AA AA

8AAAamp

A A

4 AA


0 4 8 12 16 20 24



120 -






15

00

100 121 143 165 187 209 231 253 275

Day of the Year

22

105


120-





land 1984 15 0

0 0 100 121 143 165 187 209 231 253 275

Day of the Year


180-


(line) versus 135




45 North Dakota

1985

0 100 120 140 160 180 200 220 240 280

Day of the Year

22

23






24


30

SIM = -0959 + 113(OBS) r =0905 A

25 a

Figure 9 Simulated

A20versus b0 20

Aobserved

end-ofshy15 -

season E

-1 6

amp biomass

yield n =40

11line0I I I I I

0 5 10 15 20 25 30


Conclusions



25


References












26














27














28













29














13



Stress Factors





14











15

i








En





100 SIM = 1935 + 0726 (OBS) r = 0921

90 - Figure 4



7 An= 41 so A a

- 40

30


20 30 40 50 60 70 80 90 100


17






18

2


8

SIM = 182 + 0708 (OBS) r = 0473 A Figure5




growing season

n =32


0 1 2 3 4 5 6 7 8





19


6000

5250

4500


3750

3000

2250

1500

[(symbols)


750

000 100 121 143 165 187 209 291 253 275

Day of the Year


6000





0 0a Scotland

1500 1984

750

000 100 121 142 163 185 206 227 248 270

Day of the Year

20


2500


1875 0 (line) versus 1 15s62

observed (symbols)


9-37 -Russet


312 0 1985

000c 100 120 140 160 180 200 220 240 260

Day of the Year




21


24


Aversus


12 n=54

AA AA

8AAAamp

A A

4 AA


0 4 8 12 16 20 24



120 -






15

00

100 121 143 165 187 209 231 253 275

Day of the Year

22

105


120-





land 1984 15 0

0 0 100 121 143 165 187 209 231 253 275

Day of the Year


180-


(line) versus 135




45 North Dakota

1985

0 100 120 140 160 180 200 220 240 280

Day of the Year

22

23






24


30

SIM = -0959 + 113(OBS) r =0905 A

25 a

Figure 9 Simulated

A20versus b0 20

Aobserved

end-ofshy15 -

season E

-1 6

amp biomass

yield n =40

11line0I I I I I

0 5 10 15 20 25 30


Conclusions



25


References












26














27














28













29



Stress Factors





14











15

i








En





100 SIM = 1935 + 0726 (OBS) r = 0921

90 - Figure 4



7 An= 41 so A a

- 40

30


20 30 40 50 60 70 80 90 100


17






18

2


8

SIM = 182 + 0708 (OBS) r = 0473 A Figure5




growing season

n =32


0 1 2 3 4 5 6 7 8





19


6000

5250

4500


3750

3000

2250

1500

[(symbols)


750

000 100 121 143 165 187 209 291 253 275

Day of the Year


6000





0 0a Scotland

1500 1984

750

000 100 121 142 163 185 206 227 248 270

Day of the Year

20


2500


1875 0 (line) versus 1 15s62

observed (symbols)


9-37 -Russet


312 0 1985

000c 100 120 140 160 180 200 220 240 260

Day of the Year




21


24


Aversus


12 n=54

AA AA

8AAAamp

A A

4 AA


0 4 8 12 16 20 24



120 -






15

00

100 121 143 165 187 209 231 253 275

Day of the Year

22

105


120-





land 1984 15 0

0 0 100 121 143 165 187 209 231 253 275

Day of the Year


180-


(line) versus 135




45 North Dakota

1985

0 100 120 140 160 180 200 220 240 280

Day of the Year

22

23






24


30

SIM = -0959 + 113(OBS) r =0905 A

25 a

Figure 9 Simulated

A20versus b0 20

Aobserved

end-ofshy15 -

season E

-1 6

amp biomass

yield n =40

11line0I I I I I

0 5 10 15 20 25 30


Conclusions



25


References












26














27














28













29











15

i








En





100 SIM = 1935 + 0726 (OBS) r = 0921

90 - Figure 4



7 An= 41 so A a

- 40

30


20 30 40 50 60 70 80 90 100


17






18

2


8

SIM = 182 + 0708 (OBS) r = 0473 A Figure5




growing season

n =32


0 1 2 3 4 5 6 7 8





19


6000

5250

4500


3750

3000

2250

1500

[(symbols)


750

000 100 121 143 165 187 209 291 253 275

Day of the Year


6000





0 0a Scotland

1500 1984

750

000 100 121 142 163 185 206 227 248 270

Day of the Year

20


2500


1875 0 (line) versus 1 15s62

observed (symbols)


9-37 -Russet


312 0 1985

000c 100 120 140 160 180 200 220 240 260

Day of the Year




21


24


Aversus


12 n=54

AA AA

8AAAamp

A A

4 AA


0 4 8 12 16 20 24



120 -






15

00

100 121 143 165 187 209 231 253 275

Day of the Year

22

105


120-





land 1984 15 0

0 0 100 121 143 165 187 209 231 253 275

Day of the Year


180-


(line) versus 135




45 North Dakota

1985

0 100 120 140 160 180 200 220 240 280

Day of the Year

22

23






24


30

SIM = -0959 + 113(OBS) r =0905 A

25 a

Figure 9 Simulated

A20versus b0 20

Aobserved

end-ofshy15 -

season E

-1 6

amp biomass

yield n =40

11line0I I I I I

0 5 10 15 20 25 30


Conclusions



25


References












26














27














28













29

i








En





100 SIM = 1935 + 0726 (OBS) r = 0921

90 - Figure 4



7 An= 41 so A a

- 40

30


20 30 40 50 60 70 80 90 100


17






18

2


8

SIM = 182 + 0708 (OBS) r = 0473 A Figure5




growing season

n =32


0 1 2 3 4 5 6 7 8





19


6000

5250

4500


3750

3000

2250

1500

[(symbols)


750

000 100 121 143 165 187 209 291 253 275

Day of the Year


6000





0 0a Scotland

1500 1984

750

000 100 121 142 163 185 206 227 248 270

Day of the Year

20


2500


1875 0 (line) versus 1 15s62

observed (symbols)


9-37 -Russet


312 0 1985

000c 100 120 140 160 180 200 220 240 260

Day of the Year




21


24


Aversus


12 n=54

AA AA

8AAAamp

A A

4 AA


0 4 8 12 16 20 24



120 -






15

00

100 121 143 165 187 209 231 253 275

Day of the Year

22

105


120-





land 1984 15 0

0 0 100 121 143 165 187 209 231 253 275

Day of the Year


180-


(line) versus 135




45 North Dakota

1985

0 100 120 140 160 180 200 220 240 280

Day of the Year

22

23






24


30

SIM = -0959 + 113(OBS) r =0905 A

25 a

Figure 9 Simulated

A20versus b0 20

Aobserved

end-ofshy15 -

season E

-1 6

amp biomass

yield n =40

11line0I I I I I

0 5 10 15 20 25 30


Conclusions



25


References












26














27














28













29





100 SIM = 1935 + 0726 (OBS) r = 0921

90 - Figure 4



7 An= 41 so A a

- 40

30


20 30 40 50 60 70 80 90 100


17






18

2


8

SIM = 182 + 0708 (OBS) r = 0473 A Figure5




growing season

n =32


0 1 2 3 4 5 6 7 8





19


6000

5250

4500


3750

3000

2250

1500

[(symbols)


750

000 100 121 143 165 187 209 291 253 275

Day of the Year


6000





0 0a Scotland

1500 1984

750

000 100 121 142 163 185 206 227 248 270

Day of the Year

20


2500


1875 0 (line) versus 1 15s62

observed (symbols)


9-37 -Russet


312 0 1985

000c 100 120 140 160 180 200 220 240 260

Day of the Year




21


24


Aversus


12 n=54

AA AA

8AAAamp

A A

4 AA


0 4 8 12 16 20 24



120 -






15

00

100 121 143 165 187 209 231 253 275

Day of the Year

22

105


120-





land 1984 15 0

0 0 100 121 143 165 187 209 231 253 275

Day of the Year


180-


(line) versus 135




45 North Dakota

1985

0 100 120 140 160 180 200 220 240 280

Day of the Year

22

23






24


30

SIM = -0959 + 113(OBS) r =0905 A

25 a

Figure 9 Simulated

A20versus b0 20

Aobserved

end-ofshy15 -

season E

-1 6

amp biomass

yield n =40

11line0I I I I I

0 5 10 15 20 25 30


Conclusions



25


References












26














27














28













29






18

2


8

SIM = 182 + 0708 (OBS) r = 0473 A Figure5




growing season

n =32


0 1 2 3 4 5 6 7 8





19


6000

5250

4500


3750

3000

2250

1500

[(symbols)


750

000 100 121 143 165 187 209 291 253 275

Day of the Year


6000





0 0a Scotland

1500 1984

750

000 100 121 142 163 185 206 227 248 270

Day of the Year

20


2500


1875 0 (line) versus 1 15s62

observed (symbols)


9-37 -Russet


312 0 1985

000c 100 120 140 160 180 200 220 240 260

Day of the Year




21


24


Aversus


12 n=54

AA AA

8AAAamp

A A

4 AA


0 4 8 12 16 20 24



120 -






15

00

100 121 143 165 187 209 231 253 275

Day of the Year

22

105


120-





land 1984 15 0

0 0 100 121 143 165 187 209 231 253 275

Day of the Year


180-


(line) versus 135




45 North Dakota

1985

0 100 120 140 160 180 200 220 240 280

Day of the Year

22

23






24


30

SIM = -0959 + 113(OBS) r =0905 A

25 a

Figure 9 Simulated

A20versus b0 20

Aobserved

end-ofshy15 -

season E

-1 6

amp biomass

yield n =40

11line0I I I I I

0 5 10 15 20 25 30


Conclusions



25


References












26














27














28













29

2


8

SIM = 182 + 0708 (OBS) r = 0473 A Figure5




growing season

n =32


0 1 2 3 4 5 6 7 8





19


6000

5250

4500


3750

3000

2250

1500

[(symbols)


750

000 100 121 143 165 187 209 291 253 275

Day of the Year


6000





0 0a Scotland

1500 1984

750

000 100 121 142 163 185 206 227 248 270

Day of the Year

20


2500


1875 0 (line) versus 1 15s62

observed (symbols)


9-37 -Russet


312 0 1985

000c 100 120 140 160 180 200 220 240 260

Day of the Year




21


24


Aversus


12 n=54

AA AA

8AAAamp

A A

4 AA


0 4 8 12 16 20 24



120 -






15

00

100 121 143 165 187 209 231 253 275

Day of the Year

22

105


120-





land 1984 15 0

0 0 100 121 143 165 187 209 231 253 275

Day of the Year


180-


(line) versus 135




45 North Dakota

1985

0 100 120 140 160 180 200 220 240 280

Day of the Year

22

23






24


30

SIM = -0959 + 113(OBS) r =0905 A

25 a

Figure 9 Simulated

A20versus b0 20

Aobserved

end-ofshy15 -

season E

-1 6

amp biomass

yield n =40

11line0I I I I I

0 5 10 15 20 25 30


Conclusions



25


References












26














27














28













29


6000

5250

4500


3750

3000

2250

1500

[(symbols)


750

000 100 121 143 165 187 209 291 253 275

Day of the Year


6000





0 0a Scotland

1500 1984

750

000 100 121 142 163 185 206 227 248 270

Day of the Year

20


2500


1875 0 (line) versus 1 15s62

observed (symbols)


9-37 -Russet


312 0 1985

000c 100 120 140 160 180 200 220 240 260

Day of the Year




21


24


Aversus


12 n=54

AA AA

8AAAamp

A A

4 AA


0 4 8 12 16 20 24



120 -






15

00

100 121 143 165 187 209 231 253 275

Day of the Year

22

105


120-





land 1984 15 0

0 0 100 121 143 165 187 209 231 253 275

Day of the Year


180-


(line) versus 135




45 North Dakota

1985

0 100 120 140 160 180 200 220 240 280

Day of the Year

22

23






24


30

SIM = -0959 + 113(OBS) r =0905 A

25 a

Figure 9 Simulated

A20versus b0 20

Aobserved

end-ofshy15 -

season E

-1 6

amp biomass

yield n =40

11line0I I I I I

0 5 10 15 20 25 30


Conclusions



25


References












26














27














28













29


2500


1875 0 (line) versus 1 15s62

observed (symbols)


9-37 -Russet


312 0 1985

000c 100 120 140 160 180 200 220 240 260

Day of the Year




21


24


Aversus


12 n=54

AA AA

8AAAamp

A A

4 AA


0 4 8 12 16 20 24



120 -






15

00

100 121 143 165 187 209 231 253 275

Day of the Year

22

105


120-





land 1984 15 0

0 0 100 121 143 165 187 209 231 253 275

Day of the Year


180-


(line) versus 135




45 North Dakota

1985

0 100 120 140 160 180 200 220 240 280

Day of the Year

22

23






24


30

SIM = -0959 + 113(OBS) r =0905 A

25 a

Figure 9 Simulated

A20versus b0 20

Aobserved

end-ofshy15 -

season E

-1 6

amp biomass

yield n =40

11line0I I I I I

0 5 10 15 20 25 30


Conclusions



25


References












26














27














28













29


24


Aversus


12 n=54

AA AA

8AAAamp

A A

4 AA


0 4 8 12 16 20 24



120 -






15

00

100 121 143 165 187 209 231 253 275

Day of the Year

22

105


120-





land 1984 15 0

0 0 100 121 143 165 187 209 231 253 275

Day of the Year


180-


(line) versus 135




45 North Dakota

1985

0 100 120 140 160 180 200 220 240 280

Day of the Year

22

23






24


30

SIM = -0959 + 113(OBS) r =0905 A

25 a

Figure 9 Simulated

A20versus b0 20

Aobserved

end-ofshy15 -

season E

-1 6

amp biomass

yield n =40

11line0I I I I I

0 5 10 15 20 25 30


Conclusions



25


References












26














27














28













29

105


120-





land 1984 15 0

0 0 100 121 143 165 187 209 231 253 275

Day of the Year


180-


(line) versus 135




45 North Dakota

1985

0 100 120 140 160 180 200 220 240 280

Day of the Year

22

23






24


30

SIM = -0959 + 113(OBS) r =0905 A

25 a

Figure 9 Simulated

A20versus b0 20

Aobserved

end-ofshy15 -

season E

-1 6

amp biomass

yield n =40

11line0I I I I I

0 5 10 15 20 25 30


Conclusions



25


References












26














27














28













29






24


30

SIM = -0959 + 113(OBS) r =0905 A

25 a

Figure 9 Simulated

A20versus b0 20

Aobserved

end-ofshy15 -

season E

-1 6

amp biomass

yield n =40

11line0I I I I I

0 5 10 15 20 25 30


Conclusions



25


References












26














27














28













29


30

SIM = -0959 + 113(OBS) r =0905 A

25 a

Figure 9 Simulated

A20versus b0 20

Aobserved

end-ofshy15 -

season E

-1 6

amp biomass

yield n =40

11line0I I I I I

0 5 10 15 20 25 30


Conclusions



25


References












26














27














28













29


References












26














27














28













29














27














28













29














28













29













29

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A Simulation Model for Potato Growth and Development ...deficit are simulated in SUBSTOR-Potato and...

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